Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset

By Kohei Ozaki, Shuhei Yokoo. 2019

36 citations recorded by the source corpus.

The Google-Landmarks-v2 dataset is the biggest worldwide landmarks dataset characterized by a large magnitude of noisiness and diversity. We present a novel landmark retrieval/recognition system, robust to a noisy and diverse dataset, by our team, smlyaka. Our approach is based on deep convolutional neural networks with metric learning, trained by cosine-softmax based losses. Deep metric learning methods are usually sensitive to noise, and it could hinder to learn a reliable metric. To address this issue, we develop an automated data cleaning system. Besides, we devise a discriminative re-ranking method to address the diversity of the dataset for landmark retrieval. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge and 3rd place in the Google Landmark Recognition 2019 challenge on Kaggle.

Subfields: Archeology, Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Federated Visual Classification with Real-World Data Distribution (2020, background): “In this set, images are discarded if the computed local geometric features from which cannot be matched to at least two other images with the same label [22].”
Unifying Deep Local and Global Features for Image Search (2020, background): “ the top database images using local feature matching – to get the best of both worlds. Such a hybrid approach gained popularity in visual localization [55,50] and instance-level recognition problems [43,62]. Today, most systems that rely on both these types of features need to separately extract ea”